UAV-Enabled Wireless Networks for Integrated Sensing and Learning-Oriented Communication
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866913488029351936 |
|---|---|
| author | Zhuang, Wenhao He, Xinyu Mao, Yuyi Liu, Juan |
| author_facet | Zhuang, Wenhao He, Xinyu Mao, Yuyi Liu, Juan |
| contents | Future wireless networks are envisioned to support both sensing and artificial intelligence (AI) services. However, conventional integrated sensing and communication (ISAC) networks may not be suitable due to the ignorance of diverse task-specific data utilities in different AI applications. In this letter, a full-duplex unmanned aerial vehicle (UAV)-enabled wireless network providing sensing and edge learning services is investigated. To maximize the learning performance while ensuring sensing quality, a convergence-guaranteed iterative algorithm is developed to jointly determine the uplink time allocation, as well as UAV trajectory and transmit power. Simulation results show that the proposed algorithm significantly outperforms the baselines and demonstrate the critical tradeoff between sensing and learning performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_00405 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | UAV-Enabled Wireless Networks for Integrated Sensing and Learning-Oriented Communication Zhuang, Wenhao He, Xinyu Mao, Yuyi Liu, Juan Networking and Internet Architecture Future wireless networks are envisioned to support both sensing and artificial intelligence (AI) services. However, conventional integrated sensing and communication (ISAC) networks may not be suitable due to the ignorance of diverse task-specific data utilities in different AI applications. In this letter, a full-duplex unmanned aerial vehicle (UAV)-enabled wireless network providing sensing and edge learning services is investigated. To maximize the learning performance while ensuring sensing quality, a convergence-guaranteed iterative algorithm is developed to jointly determine the uplink time allocation, as well as UAV trajectory and transmit power. Simulation results show that the proposed algorithm significantly outperforms the baselines and demonstrate the critical tradeoff between sensing and learning performance. |
| title | UAV-Enabled Wireless Networks for Integrated Sensing and Learning-Oriented Communication |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2409.00405 |